System and method for removing hard tissue in CT image

ABSTRACT

A system and method for CT image reconstruction are provided. The method may include: obtaining raw data set related to an object; generating a first image set based on the raw data set, wherein the first image set includes a first full quality image and a first max field of view image; generating one or more reference images based on the first max field of view image; generating a first bone information image based on the one or more reference images; generating a second image set based on the raw data set, wherein the second image set includes a second full quality image; generating a second bone information image based on the one or more reference images; correcting hardening beam artifact of the second full quality image based on the second bone information image to generate a hardening beam artifact corrected image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/798,246 filed on Oct. 30, 2017, which is a continuation ofInternational Application No. PCT/CN2017/099353 filed on Aug. 28, 2017,the entire contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure generally relates to computed tomography (CT),and more specifically relates to a system and method for correctingartifact due to hard tissue in CT image.

BACKGROUND

To provide a clear view of soft tissue, hard tissue sometimes needs tobe removed from a CT image. However, the traditional way for hard tissueremoval and hardening beam artifact correction consumes much time whentwo or more different CT images having different reconstructionparameters need to be generated and corrected. The present disclosureprovides an efficient method and system for correcting artifact due tohard tissue in a CT image.

SUMMARY

In a first aspect of the present disclosure, a system for CT imagereconstruction is provided. The system may include at least oneprocessor and instructions. When the at least one processor executes theinstructions, the at least one processor may be directed to perform oneor more of the following operations. The at least one processor mayobtain a raw data set related to an object. The at least one processormay generate a first image set based on the raw data set, wherein thefirst image set may include a first full quality image and a first maxfield of view image. The at least one processor may generate one or morereference images based on the first max field of view image. The atleast one processor may generate a first bone information image based onthe one or more reference images. The at least one processor maygenerate a second image set based on the raw data set, wherein thesecond image set includes a second full quality image. The at least oneprocessor may generate a second bone information image based on the oneor more reference images. The at least one processor may correcthardening beam artifact of the second full quality image based on thesecond bone information image to generate a hardening beam artifactcorrected image.

In some embodiments, a first image thickness of the first boneinformation image may be greater than an image thickness of thereference image, a second image thickness of the second bone informationimage may be greater than the image thickness of the reference image,and the first image thickness may be different from the second imagethickness.

In some embodiments, a first image increment of the first boneinformation image may be greater than an image increment of thereference image, a second image increment of the second bone informationimage may be greater than the image increment of the reference image,and the first image increment may be different from the second imageincrement.

In some embodiments, a field of view of the first full quality image maybe smaller than a field of view of the first max field of view image.

In some embodiments, the at least one processor may remove hard tissuefrom the first full quality image based on the first max field of viewimage to generate a hard tissue corrected image.

In some embodiments, to generate the first bone information image, theat least one processor may stack one or more reference images based onan image thickness of the first bone information image and an imagethickness of the reference image.

In some embodiments, to generate the second bone information image, theat least one processor may stack one or more reference images based onan image thickness of the second bone information image and an imagethickness of the reference image.

In some embodiments, an image thickness of the first full quality imageand an image thickness of the second full quality image may bedifferent.

In some embodiments, an image increment of the first full quality imageand an image increment of the second full quality image may bedifferent.

In some embodiments, the at least one processor may output the firstbone information image or the second bone information image to a user.

In a second aspect of the present disclosure, a method for CT imagereconstruction is provided. The method may include one or more of thefollowing operations. A raw data set related to an object may beobtained. A first image set may be generated based on the raw data set,wherein the first image set includes a first full quality image and afirst max field of view image. One or more reference images based on thefirst max field of view image may be generated. A first bone informationimage based on the one or more reference images may be generated by aprocessor. A second image set may be generated based on the raw dataset, wherein the second image set includes a second full quality image.A second bone information image may be generated based on the one ormore reference images. Hardening beam artifact of the second fullquality image may be corrected based on the second bone informationimage to generate a hardening beam artifact corrected image.

In some embodiments, a first image thickness of the first boneinformation image may be greater than an image thickness of thereference image, a second image thickness of the second bone informationimage may be greater than the image thickness of the reference image,and the first image thickness may be different from the second imagethickness.

In some embodiments, a first image increment of the first boneinformation image may be greater than an image increment of thereference image, a second image increment of the second bone informationimage may be greater than the image increment of the reference image,and the first image increment may be different from the second imageincrement.

In some embodiments, a field of view of the first full quality image maybe smaller than a field of view of the first max field of view image.

In some embodiments, hard tissue from the first full quality image maybe removed based on the first max field of view image to generate a hardtissue corrected image.

In some embodiments, to generate the first bone information image, oneor more reference images may be stacked based on an image thickness ofthe first bone information image and an image thickness of the referenceimage.

In some embodiments, to generate the second bone information image, oneor more reference images may be stacked based on an image thickness ofthe second bone information image and an image thickness of thereference image.

In some embodiments, an image thickness of the first full quality imageand an image thickness of the second full quality image may bedifferent.

In some embodiments, an image increment of the first full quality imageand an image increment of the second full quality image may bedifferent.

In a third aspect of the present disclosure, a non-transitory computerreadable medium is provided. The non-transitory computer readable mediummay include executable instructions. When at least one processorexecutes the instructions, the at least one processor may effectuate amethod including one or more of the following operations. A raw data setrelated to an object may be obtained. A first image set may be generatedbased on the raw data set, wherein the first image set includes a firstfull quality image and a first max field of view image. One or morereference images based on the first max field of view image may begenerated. A first bone information image based on the one or morereference images may be generated by a processor. A second image set maybe generated based on the raw data set, wherein the second image setincludes a second full quality image. A second bone information imagemay be generated based on the one or more reference images. Hardeningbeam artifact of the second full quality image may be corrected based onthe second bone information image to generate a hardening beam artifactcorrected image.

In a fourth aspect of the present disclosure, a method for CT imagereconstruction is provided. The method may include one or more of thefollowing operations. A raw data set related to an object may beobtained. A set of full quality images with a target image thickness maybe generated based on the raw data set. A set of original boneinformation images with an original image thickness may be generatedbased on the raw data set. A set of target bone information images witha target image thickness may be generated based on the set of originalbone information images. A set of hardening beam artifact correctedimages may be generated by correcting hardening beam artifact of thefull quality images based on the set of target bone information imagesto generate.

In some embodiments, the target image thickness may be greater than theoriginal image thickness.

In some embodiments, to generate a set of original bone informationimages, a set of max field of view images may be generated based on theraw data set, and a set of original bone information images may begenerated based on the max field of view images.

In some embodiments, hard tissue from the set of full quality images maybe removed based on the set of target bone information images togenerate a set of hard tissue corrected images.

In a fifth aspect of the present disclosure, a system for CT imagereconstruction is provided. The system may include a data acquisitionunit, an image reconstruction unit, a correction image generation unitand a correction unit. The data acquisition unit may be configured toobtain a raw data set related to an object. The image reconstructionunit may be configured to generate a first image set based on the rawdata set, wherein the first image set includes a first full qualityimage and a first max field of view image, and generate a second imageset including a second full quality image. The correction imagegeneration unit may be configured to generate one or more referenceimages based on the first max field of view image, and generate a firstbone information image and a second bone information image based on theone or more reference images. The correction unit may be configured tocorrect hardening beam artifact of the second full quality image basedon the second bone information image to generate a hardening beamartifact corrected image.

In some embodiments, a first image thickness of the first boneinformation image may be greater than an image thickness of thereference image, a second image thickness of the second bone informationimage may be greater than the image thickness of the reference image,and the first image thickness is different from the second imagethickness.

In some embodiments, a first image increment of the first boneinformation image may be greater than an image increment of thereference image, a second image increment of the second bone informationimage is greater than the image increment of the reference image, andthe first image increment is different from the second image increment.

In some embodiments, a field of view of the first full quality image maybe smaller than a field of view of the first max field of view image.

In some embodiments, the correction unit may be further configured toremove hard tissue from the first full quality image based on the firstmax field of view image to generate a hard tissue corrected image.

In some embodiments, to generate the first bone information image, thecorrection image generation unit may be further configured to stack oneor more reference images based on an image thickness of the first boneinformation image and an image thickness of the reference image.

In some embodiments, to generate the second bone information image, thecorrection image generation unit may be further configured to stack oneor more reference images based on an image thickness of the second boneinformation image and an image thickness of the reference image.

In some embodiments, the correction image generation unit may be furtherconfigured to output the first bone information image or the second boneinformation image to a user.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1-A and FIG. 1-B are schematic diagrams illustrating exemplary CTsystems according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating an architecture of acomputing device according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4 is a schematic diagram illustrating an exemplary processoraccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart of an exemplary process for generating a fullquality image set with hard tissue removed and hardening beam artifactcorrected according to some embodiments of the present disclosure;

FIG. 6 is a flowchart of an exemplary process for generating a referenceimage according to some embodiments of the present disclosure;

FIG. 7 is a flowchart of an exemplary process for hard tissue removingand hardening beam artifact correcting process according to someembodiments of the present disclosure; and

FIG. 8 is a flowchart of an exemplary process for checking workingstatus based on the bone information image according to some embodimentsof the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in a firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedof connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for non-invasive imaging,such as for disease diagnosis or research purposes. In some embodiments,the imaging system may be a computed tomography (CT) system, an emissioncomputed tomography (ECT) system, a magnetic resonance imaging (MRI)system, an ultrasonography system, an X-ray photography system, apositron emission tomography (PET) system, or the like, or anycombination thereof.

The following description is provided to help better understanding CTimage reconstruction methods or systems. The term “image” used in thisdisclosure may refer to a 2D image, a 3D image, a 4D image, or anyrelated image data (e.g., CT data, projection data corresponding to theCT data). This is not intended to limit the scope the presentdisclosure. For persons having ordinary skills in the art, a certainamount of variations, changes, and/or modifications may be deductedunder guidance of the present disclosure. Those variations, changes,and/or modifications do not depart from the scope of the presentdisclosure.

An aspect of the present disclosure relates to systems and methods forgenerating and correcting different CT images based on a raw data set.According to the present disclosure, a set of reference images may begenerated, and bone information images of different thicknesses and/orimage increments may be generated based on the set of reference images.Another aspect of the present disclosure relates to systems and methodsfor identifying abnormalities of CT systems. According to the presentdisclosure, abnormalities of a CT system may be identified bydetermining difference between reference images generated from differentraw data sets.

FIG. 1-A and FIG. 1-B are schematic diagram illustrating an exemplary CTsystem according to some embodiments of the present disclosure. The CTsystem may include a CT scanner 110, a network 120, a terminal 130, aprocessing engine 140, and a storage 150. The connection between thecomponents in the CT system 100 may be variable. Merely by way ofexample, as illustrated in FIG. 1-A, the CT scanner 110 may be connectedto the processing engine 140 through the network 120. As anotherexample, as illustrated in FIG. 1-B, the CT scanner 110 may be connectedto the processing engine 140 directly.

The CT scanner 110 may include a gantry 111, a detector 112, a detectingregion 113, a subject table 114, and a radioactive scanning source 115.The gantry 111 may support the detector 112 and the radioactive scanningsource 115. A subject may be placed on the subject table 114 to bescanned. The radioactive scanning source 115 may emit radioactive raysto the subject. The detector 112 may detect radiation events (e.g.,gamma photons) emitted from the detecting region 113. In someembodiments, the detector 112 may include a plurality of detector units.The detector units may include a scintillation detector (e.g., a cesiumiodide detector) or a gas detector. The detector unit may be asingle-row detector or a multi-rows detector.

The network 120 may facilitate exchange of information and/or data. Insome embodiments, one or more components in the CT system 100 (e.g., theCT scanner 110, the terminal 130, the processing engine 140, or thestorage 150) may send information and/or data to other component(s) inthe CT system 100 via the network 120. For example, the processingengine 140 may obtain image data from the CT scanner 110 via the network120. As another example, the processing engine 140 may obtain userinstructions from the terminal 130 via the network 120. In someembodiments, the network 120 may be any type of wired or wirelessnetwork, or combination thereof. Merely by way of example, the network120 may include a cable network, a wireline network, an optical fibernetwork, a tele communications network, an intranet, an Internet, alocal area network (LAN), a wide area network (WAN), a wireless localarea network (WLAN), a metropolitan area network (MAN), a wide areanetwork (WAN), a public telephone switched network (PSTN), a Bluetoothnetwork, a ZigBee network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network120 may include one or more network access points. For example, thenetwork 120 may include wired or wireless network access points such asbase stations and/or internet exchange points through which one or morecomponents of the CT system 100 may be connected to the network 120 toexchange data and/or information.

The terminal 130 include a mobile device 130-1, a tablet computer 130-2,a laptop computer 130-3, or the like, or any combination thereof. Insome embodiments, the mobile device 130-1 may include a smart homedevice, a wearable device, a smart mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include a smartlighting device, a control device of an intelligent electricalapparatus, a smart monitoring device, a smart television, a smart videocamera, an interphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, a smartfootgear, a smart glass, a smart helmet, a smart watch, a smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistance (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a GoogleGlass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments,the terminal 130 may be part of the processing engine 140. In someembodiments, the terminal 130 may be connected to or otherwisecommunicate with the processing engine 140.

The processing engine 140 may process data and/or information obtainedfrom the CT scanner 110, the terminal 130, or the storage 150. In someembodiments, the processing engine 140 may be a single server, or aserver group. The server group may be centralized, or distributed. Insome embodiments, the processing engine 140 may be local or remote. Forexample, the processing engine 140 may access information and/or datastored in the CT scanner 110, the terminal 130, and/or the storage 150via the network 120. As another example, the processing engine 140 maybe directly connected to the CT scanner 110, the terminal 130 and/or thestorage 150 to access stored information and/or data. In someembodiments, the processing engine 140 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof. In some embodiments, the processing engine 140 maybe implemented on a computing device 200 having one or more componentsillustrated in FIG. 2 in the present disclosure.

The storage 150 may store data and/or instructions. In some embodiments,the storage 150 may store data obtained from the terminal 130 and/or theprocessing engine 140. In some embodiments, the storage 150 may storedata and/or instructions that the processing engine 140 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage 150 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drives, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (PEROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage 150 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage 150 may be connected to the network 120to communicate with one or more components in the CT system 100 (e.g.,the processing engine 140, the terminal 130). One or more components inthe CT system 100 may access the data or instructions stored in thestorage 150 via the network 120. In some embodiments, the storage 150may be directly connected to or communicate with one or more componentsin the CT system 100 (e.g., the processing engine 140, the terminal130). In some embodiments, the storage 150 may be part of the processingengine 140.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing engine 140 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2-A, the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing engine 140 in accordance withtechniques described herein. The computer instructions may includeroutines, programs, objects, components, data structures, procedures,modules, and functions, which perform particular functions describedherein. For example, the processor 210 may process image data obtainedfrom the CT scanner 110, the terminal 130, the storage 150, or any othercomponent of the CT system 100. In some embodiments, the processor 210may include a microcontroller, a microprocessor, a reduced instructionset computer (RISC), an application specific integrated circuits(ASICs), an application-specific instruction-set processor (ASIP), acentral processing unit (CPU), a graphics processing unit (GPU), aphysics processing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be note that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both step A and step B, it should be understood that step A andstep B may also be performed by two different processors jointly orseparately in the computing device 200 (e.g., a first processor executesstep A and a second processor executes step B, or the first and secondprocessors jointly execute steps A and B).

The storage 220 may store data/information obtained from the CT scanner110, the terminal 130, the storage 150, or any other component of the CTsystem 100. In some embodiments, the storage 220 may include a massstorage, a removable storage, a volatile read-and-write memory, aread-only memory (ROM), or the like, or any combination thereof. Forexample, the mass storage may include a magnetic disk, an optical disk,a solid-state drives, etc. The removable storage may include a flashdrive, a floppy disk, an optical disk, a memory card, a zip disk, amagnetic tape, etc. The volatile read-and-write memory may include arandom access memory (RAM). The RAM may include a dynamic RAM (DRAM), adouble date rate synchronous dynamic RAM (DDR SDRAM), a static RAM(SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc.The ROM may include a mask ROM (MROM), a programmable ROM (PROM), anerasable programmable ROM (PEROM), an electrically erasable programmableROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile diskROM, etc. In some embodiments, the storage 220 may store one or moreprograms and/or instructions to perform exemplary methods described inthe present disclosure.

The I/O 230 may input or output signals, data, or information. In someembodiments, the I/O 230 may enable a user interaction with theprocessing engine 140. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input device may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output device may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing engine 140 and theCT scanner 110, the terminal 130, or the storage 150. The connection maybe a wired connection, a wireless connection, or combination of boththat enables data transmission and reception. The wired connection mayinclude electrical cable, optical cable, telephone wire, or the like, orany combination thereof. The wireless connection may include Bluetooth,Wi-Fi, WiMax, WLAN, ZigBee, mobile network (e.g., 3G, 4G, 5G, etc.), orthe like, or a combination thereof. In some embodiments, thecommunication port 240 may be a standardized communication port, such asRS232, RS485, etc. In some embodiments, the communication port 240 maybe a specially designed communication port. For example, thecommunication port 240 may be designed in accordance with the digitalimaging and communications in medicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminal 130 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS, Android,Windows Phone, etc.) and one or more applications 380 may be loaded intothe memory 360 from the storage 390 in order to be executed by the CPU340. The applications 380 may include a browser or any other suitablemobile apps for receiving and rendering information relating to imageprocessing or other information from the processing engine 140. Userinteractions with the information stream may be achieved via the I/O 350and provided to the processing engine 140 and/or other components of theCT system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to the blood pressure monitoring asdescribed herein. A computer with user interface elements may be used toimplement a personal computer (PC) or other type of work station orterminal device, although a computer may also act as a server ifappropriately programmed. It is believed that those skilled in the artare familiar with the structure, programming and general operation ofsuch computer equipment and as a result the drawings should beself-explanatory.

FIG. 4 is a schematic diagram illustrating an exemplary processingengine 140 according to some embodiments of the present disclosure.Processing engine 140 may process data obtained from or via CT scanner110, processor 210, storage 220, input/output (I/O) 230, communicationport 240, or the like, or any combination thereof. The data processed byprocessing engine 140 may include back projection data, forwardprojection data, correction data, filtered data, image data (e.g.,original image data), or the like, or any combination thereof.Processing engine 140 may perform operations including, for example,data preprocessing, image reconstruction, image correction, imagecomposition, lookup table creation, or the like, or any combinationthereof. Processing engine 140 may include a data acquisition unit 410,an image reconstruction unit 420, a correction image generation unit430, and a correction unit 440. Processing engine 140, or a portionthereof, may be implemented on the computing device 200 as illustratedin FIG. 2, or the mobile device as illustrated in FIG. 3.

Data acquisition unit 410 may obtain a raw data set related to an objectunder examination. The raw data set may include a plurality of raw datarelated to the object under examination. The data acquisition unit 410may obtain the raw data set or the raw data via the detector 112 or thestorage 150. The term “raw data” may refer to the data that may bedetected by the detector 112, and the raw data may be utilized toconstruct a CT image. The raw data may be generated by traversing X-raysthrough an object under examination. The object may include a substance,a tissue, an organ, a specimen, a body, or the like, or any combinationthereof. In some embodiments, the object may include a patient or a partthereof. The objet may include a head, a breast, a lung, a pleura, amediastinum, an abdomen, a long intestine, a small intestine, a bladder,a gallbladder, a triple warmer, a pelvic cavity, a backbone,extremities, a skeleton, a blood vessel, or the like, or any combinationthereof.

Image reconstruction unit 420 may generate a CT image set based on theraw data set obtained from data acquisition unit 410. The CT image setmay include one or more CT images. The CT image may be generated basedon a reconstruction algorithm. The reconstruction algorithm may includea Fourier slice theorem algorithm, a filtered back projection (FBP)algorithm, a fan-beam reconstruction algorithm, an iterativereconstruction algorithm, an analytic reconstruction algorithm, analgorithm based on compressed sensing (CS), or the like, or anycombination thereof.

A CT image may be a representation of a cross section of tissue of anobject (e.g., a CT image slice (or referred to as a slice for brevity)of the object) under examination having some thickness. A CT image mayinclude one or more pixels arranged in a reconstruction matrix. The sizeof the reconstruction matrix may determine number of pixels in a CTimage. A pixel value may refer to the value of a property of the pixel.For instance, a pixel value may refer to luminance value of a pixel,grey value of a pixel, color or RGB value of a pixel, saturation valueof a pixel, or the like, or any combination thereof. In a CT image, thepixel value may represent density of tissue. With different pixel valuesin the CT image, the CT image may represent structure of the slice of anobject under examination.

In some embodiments, the image reconstruction unit 420 may generatedifferent CT images with different reconstruction parameters. Thereconstruction parameter may include field of view (FOV), imagethickness, image increment, kernel, or the like, or any combinationthereof. Thickness of a slice of an object may be referred as slicethickness. The position of the slice of an object may be referred asslice position. Image thickness may refer to nominal width ofreconstructed image along z axis. The z axis may be parallel to movingdirection of the subject table 114. The image thickness may bedetermined based on the slice thickness. In some embodiments, the imagethickness of a CT image may be equal to or less than the slicethickness. The larger image thickness is, the higher resolution of thereconstructed image is. For a slice of the object under examination, ata certain slice position with a certain slice thickness, one or more CTimages may be reconstructed to represent the slice. An image incrementmay refer to a distance between two consecutive CT images in terms oftheir slice positions in a CT image set including a stack of CT imageslices. A field of view (FOV) may have the diameter of a CT image. Theuse of a small FOV may allow increased spatial resolution in a CT image,because the whole reconstruction matrix of the CT image may be used forreconstructing a smaller region than is the case with a larger FOV. CTimages in a CT image set may be reconstructed based on the samereconstruction parameter(s). Accordingly, the reconstructionparameter(s) of the CT image set may be considered as the reconstructionparameter(s) of the CT images in the CT image set.

The correction image generation unit 430 may generate bone information(bone information) image set based on the CT image generated by theimage reconstruction unit 420. The bone information image set mayinclude one or more bone information images. The bone information imagemay include beam hardening artifact of a CT image. Beam hardeningartifact may be observed in a CT image when a polychromatic X-ray beampasses through an object where the lower energy photons are absorbedleaving only the higher energy photons passing through the object anddetected. The bone information image may correct the beam hardeningartifact in the CT image. In some embodiments, the bone informationimage may be generated based on the same reconstruction parameter(s) ofthe CT image generated by the image reconstruction unit 420. Forexample, if the image reconstruction unit 420 reconstruct a CT imagewith an image thickness A and image increment B, a bone informationimage may also have the image thickness A and image increment B. In someembodiments, the bone information image may be generated based on areference image. The reference image may refer to a bone informationimage reconstructed with a small image thickness and/or image increment.In some embodiments, the image thickness and/or image increment of thebone information image may be greater than the image thickness and/orimage increment of the reference image. In some embodiments, the imagethickness and/or image increment of the bone information image may be anintegral multiple of image thickness and/or image increment of thereference image. In some embodiments, the reference image may bereconstructed with the smallest image thickness and/or the smallestimage increment that the CT system 100 may achieve. Some of thereconstruction parameters of the bone information image and thereference image may be the same, such as FOV, kernel, etc., and some maybe different, such as image thickness and image increment. The boneinformation image for a certain slice position may be generated bystacking a certain number of consecutive reference images thatcorrespond to a same or similar portion of the object compared to the tothe bone information image or the CT image slice of that slice position.For example, for a bone information image of a slice having an imagethickness of 20 mm and image increment of 20 mm, and the reference imageof the slice having an image thickness of 10 mm and image increment of10 mm, two reference images of the slice may be stacked to generate thebone information image.

The hardening beam artifact may have two distinct appearances, streaksor dark bands in a CT image, and cupping artifact in a CT image. Suchartifact in the CT image may be misinterpreted by a user (e.g., adoctor) as a feature of some disease. For example, hardening beamartifact with appearance of dark bands in a CT image may bemisinterpreted as a feature of a tumor. Since bone information image mayhave characteristic of the hardening beam artifact, a bone informationset may be used by a user (e.g., a doctor) to distinguish the hardeningbeam artifact from a feature of some disease. The bone information imageset may be presented in the form of a bone information image set or abone information image. In some embodiments, the correction imagegeneration unit 430 may output a bone information image set or a boneinformation image to a user (e.g., a doctor) via the I/O 230.

In some embodiments, the correction unit 440 may determine the workingstatus of the CT system 100 when performing scans of a first object anda second object based on the similarity between a first reference imageset and a second reference image set relating to the two objects.Different bone information image sets may be generated based ondifferent raw data sets obtained by scanning the two different objects.Normally, a first bone information image set of the first object may besimilar to a second bone information image set of the second object,considering that a bone information image set represent hardening beamartifact caused by hard tissue, and the proportion of hard tissuerelative to soft tissue may be similar between different objects (e.g.,different patients under examination). If the first bone information setis different from the second bone information set (e.g., the similarityof the first bone information set and the second bone information set isunder a threshold), the working status of the CT system 100 may beconsidered to have changed between the scanning of the first object andthe scanning of the second object. The correction unit 440 may identifyabnormalities of the CT system 100 based on whether the working statusof the CT system 100 has changed. In some embodiments, the workingstatus of the CT system 100 may include the working status of thedetector 112 (e.g., whether the detector 112 may work at a predeterminedtemperature) and the working status of the radioactive scanning source115 (e.g., whether the radioactive scanning source 115 may emit apredetermined amount of X-ray).

The correction unit 440 may be configured to correct the CT imagegenerated by the image reconstruction unit 420. The correction unit 440may remove hard tissue in the CT image generated in the imagereconstruction unit 420 to generate a hard tissue corrected image. Thehard tissue corrected image may include removed or reduced hard tissue.The correction unit 440 may correct hardening beam artifact in the CTimage generated by the image reconstruction unit 420 to generate a beamhardening artifact corrected image. The beam hardening artifactcorrected image may include removed or reduced beam hardening artifact.The correction may be performed by changing the pixel value of a pixelin the CT image. For example, to remove hard tissue in a CT image,correction unit 440 may change the pixel value of a pixel whose pixelvalue exceeds a threshold. As another example, to correct hardening beamartifact in a CT image, correction unit 440 may change the pixel valueof a pixel based on the bone information image.

FIG. 5 is a flowchart of an exemplary process for generating a fullquality image set according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of processillustrated in FIG. 5 for generating a full quality image set may beimplemented in the CT system 100 illustrated in FIG. 1. For example, theprocess illustrated in FIG. 5 may be stored in the storage 150 in theform of instructions, and invoked and/or executed by the processingengine 140 (e.g., the processor 210 of the computing device 200 asillustrated in FIG. 2, the CPU 340 of the mobile device 300 asillustrated in FIG. 3).

In 510, the data acquisition 410 may obtain a raw data set related to anobject under examination. The raw data set may include a plurality ofraw data related to the object under examination. The data acquisition410 may obtain the raw data set via the CT scanner 110 or the storage150. The raw data set may be generated by emitting X-rays toward theobject under examination.

In 520, the image reconstruction unit 420 may generate a full qualityimage set based on the raw data set. The full quality image set mayinclude one or more full quality images. The term “full quality image”may refer to a CT image with a small FOV (e.g., 10 cm). Details of softtissue may be observed in a full quality image. As used herein, softtissue may refer to a tissue that connects, supports, or surroundsanother structure and/or organ of the body, not being hard tissue suchas bone. In some embodiments, the soft tissue may include tendons,ligaments, fascia, skin, fibrous tissues, fat, and synovial membranes(which are connective tissue), muscles, nerves and blood vessels (whichare not connective tissue), or the like, or any combination thereof. Thehard tissue may refer to a tissue that is mineralized and has a firmintercellular matrix. In some embodiments, the hard tissue may includebone, tooth enamel, dentin, and cementum, or the like, or anycombination thereof.

In some embodiments, the reconstruction of the full quality image imagesmay be based on techniques including Fourier slice theorem, filteredback projection algorithm, fan-beam reconstruction, iterativereconstruction, etc. The image reconstruction unit 420 may reconstructthe full quality image based on a set of reconstruction parameters, suchas a field of view (FOV), image thickness, image increment, kernel, orthe like, or any combination thereof.

In 530, the image reconstruction unit 420 may generate a max FOV imageset based on the raw data set. The max FOV image set may include one ormore max FOV images. The term “max FOV image” may refer to a CT imagewith a large FOV (e.g., 30 cm) larger than the FOV of a correspondingfull quality image obtained based on the same raw data set. The boundarybetween soft tissue and hard tissue may be observed in the max FOVimage.

In some embodiments, the reconstruction of the max FOV image may bebased on techniques including Fourier slice theorem, filtered backprojection algorithm, fan-beam reconstruction, iterative reconstruction,etc. The image reconstruction unit 420 may reconstruct a max FOV imagebased on a set of reconstruction parameter, such as a field of view(FOV), image thickness, image increment, kernel, or the like, or anycombination thereof. In some embodiments, the FOV of a max FOV image maybe larger than that of a full quality image, and the spatial resolutionof the full quality image may be higher than that of the max FOV image.In some embodiments, image increment and/or image thickness of the maxFOV image set and the full quality image set may be the same.

In 540, the correction image generation unit 430 may generate a boneinformation image set based on the max FOV image set. The boneinformation image set may include one or more bone-info images. The term“bone information image” may refer to an image for correcting hardeningbeam artifact caused by the hard tissue. The bone information image mayrepresent hardening beam artifact of a CT image. The hardening beamartifact may have two distinct appearances, streaks or dark bands in aCT image, or cupping artifact in a CT image. Pixels of the boneinformation image may represent at least one of these appearances.

In some embodiments, a bone information image set may be generated basedon a reference image set. The reference image set may include at leastone reference image. The term “reference image” may refer to a boneinformation image constructed with a small image thickness and/or imageincrement, while other reconstruction parameters (e.g., FOV, kernel,etc.) of the reference image are the same as those of the boneinformation image. In some embodiments, the image thickness and/or imageincrement of the bone information image may be greater than the imagethickness and/or image increment of the reference image. In someembodiments, the image thickness of the bone information image may be anintegral multiple of the image thickness of the reference image. In someembodiments, the image increment of the bone information image may be anintegral multiple of image increment of the reference image. In someembodiments, the reference image may be reconstructed with the smallestimage thickness and/or smallest image increment that the CT system 100may achieve. The bone information image for a certain slice may begenerated by stacking a certain number of consecutive reference imagesthat correspond to a same or similar portion of the object compared tothe bone information image or the CT image slice of the slice position.For example, for a bone information image of a slice having an imagethickness of 20 mm and image increment of 20 mm, and the reference imageof the slice having an image thickness of 10 mm and image increment of10 mm, two reference images may be stacked to generated a boneinformation image. In some embodiments, the bone information image(s)may be used correct the hardening beam artifact in the full qualityimage. The bone information image set may be generated in advance andstored in the storage 150. The correction image generation unit 430 maygenerate the bone information image based on the reference image viaaccessing the storage 150. Detailed description of reference imagegeneration may be found in FIG. 6 and the description thereof.

In 550, the correction unit 440 may remove or reduce the hard tissue inthe full quality image based on the max FOV image set to generate a hardtissue corrected image. The hard tissue corrected image may includeremoved or reduced hard tissue. Considering that an FOV of the fullquality image may be smaller than that of the max FOV image, theintegrity of the hard tissue in the max FOV image may be better than inthe full quality image set. For example, in a CT image slicecorresponding to the head of the object under examination, a fullquality image related to the slice may display a part of the skull ofthe object under examination, while a max FOV image related to the slicemay display the whole skull. The correction unit 440 may extract shapecharacteristics (e.g., a profile curve of the hard tissue) of the hardtissue in the max FOV image, and remove the hard tissue in the fullquality image based on the shape characteristics of the hard tissue inthe full quality image.

In some embodiments, the correction image generation unit 430 may outputthe bone information image set or the bone information image to a user(e.g., a doctor) via the I/O 230, on the basis of which the user maydistinguish the hardening beam artifact and a feature of some disease.In some embodiments, the correction image generation unit 430 mayforward the bone information image set of the bone information image toa storage device (e.g., storage 150, etc.) for future use.

In 560, the correction unit 440 may correct the beam hardening artifactof the full image set based on the bone information image set togenerate a beam hardening artifact corrected image. The beam hardeningartifact corrected image may include removed or reduced beam hardeningartifact. The bone information image may be used to correct beamhardening artifact by changing pixel value of pixels in the full qualityimage which represent the beam hardening artifact. Pixel in the fullquality image may have a corresponding pixel in the bone informationimage representing same position in the slice. The correction unit 440may change the pixel value of a pixel in the max FOV image based on thepixel value of the corresponding pixel in the bone information image.

FIG. 6 is a flowchart of an exemplary process for generating a referenceimage according to some embodiments of the present disclosure. In someembodiments, one or more operations of process illustrated in FIG. 6 forgenerating a full quality image set may be implemented in the CT system100 illustrated in FIG. 1. For example, the process illustrated in FIG.6 may be stored in the storage 150 in the form of instructions, andinvoked and/or executed by the processing engine 140 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theCPU 340 of the mobile device 300 as illustrated in FIG. 3).

In 610, the image reconstruction unit 420 may generate a max FOV image.The operation 610 may be performed according to the relevant portion ofthe process illustrated in FIG. 5 and the description thereof.

In 620, the correction image generation unit 430 may segment the max FOVimage into hard tissue image and soft tissue image. The correction imagegeneration unit 430 may segment the max FOV image by assigning thepixels whose pixel values exceed a threshold to the hard tissue image,and assigning the pixels whose pixel values are below the threshold tothe soft tissue image.

In 630, the correction image generation unit 430 may forward project thehard tissue image and the soft tissue image. By the forward projection,the hard tissue image and the soft tissue image may be transformed froman image domain into a projection domain. The correction imagegeneration unit 430 may obtain projection data related to the hardtissue image and projection data related to the soft tissue image in theprojection domain.

In 640, the correction image generation unit 430 may determining thesoft tissue thickness in the soft tissue image and hard tissue thicknessin the hard tissue image based on the projection data related to thehard tissue image and the projection data related to the soft tissueimage. In some embodiments, correction image generation unit 430 maydetermine the thickness of the soft tissue in the soft tissue image andthickness of the hard tissue in the hard tissue image based on theassumption that the object under examination include only a slab of hardtissue and a slab of soft tissue. An X-ray beam may enter into the slabof hard tissue of thickness X_(B) and after passing through the slab ofhard tissue may enter into the slab of soft tissue of thickness X_(T),and then the X-ray beam may be detected by the detector 112 after itexits the soft tissue. It should be noted that even if the hard tissueand the soft tissue along the beam path may distribute differently, forthe purposes of determining the hard tissue thickness and the softtissue thickness, the assumption essentially does not change the forwardprojection data obtained in 630.

In 650, the correction image generation unit 430 may obtain correctiondata based on the soft tissue thickness and the hard tissue thickness.With a known soft tissue thickness (e.g., X_(T)) and a known hard tissuethickness (e.g., X_(B)), correction image generation unit 430 may obtaincorrection data based a correction table. In some embodiments, thecorrection table may be generated in advance and stored the storage 150.The correction table may include correction data due to the hardeningbeam artifact for the conversion of polychromatic projection data thatare impacted by the hardening beam effect (e.g., forward projection dataobtained in 630), into monochromatic projection data. The correctionimage generation unit 430 may obtain a CT image (e.g., a referenceimage) containing only the hardening beam artifact with reconstructingthe correction data. In some embodiments, the correction imagegeneration unit 430 may perform interpolation or extrapolation based onvalues available in the correct table if correction data correspondingto the soft tissue thickness X_(T) and the hard tissue thickness X_(B)do not exist in the correction table. For example, for a soft tissuethickness of 20 mm, the correction table only have correction data A fora soft tissue thickness of 22 mm and correction data B for a soft tissuethickness of 18 mm, the correction unit 440 may determine an average ofthe correction data A and correction data B as the correction data forthe soft tissue thickness of 22 mm.

In 660, the correction image generation unit 430 may generate areference image based on the correction data. With the constructionprocess, the correction image generation unit 430 may transform thecorrection data from the projection data domain to the image domain. Thereconstruction of the reference image may be based on techniquesincluding Fourier slice theorem, filtered back projection algorithm,fan-beam reconstruction, iterative reconstruction, etc. The correctionimage generation unit 430 may reconstruct the reference image based on aset of reconstruction parameters including, for example, field of view(FOV), image thickness, image increment, kernel, or the like or anycombination thereof. In some embodiments, the reference image may bereconstructed based on the smallest image thickness and/or smallestimage increment that the CT system 100 may achieve.

FIG. 7 is a flowchart of an exemplary process for removing hard tissueand/or correcting hardening beam artifact according to some embodimentsof the present disclosure. For diagnosis purposes, different fullquality images reconstructed with different reconstruction parametersmay be generated. In some embodiments, one or more operations of processillustrated in FIG. 7 for generating a full quality image set may beimplemented in the CT system 100 illustrated in FIG. 1. For example, theprocess illustrated in FIG. 7 may be stored in the storage 150 in theform of instructions, and invoked and/or executed by the processingengine 140 (e.g., the processor 210 of the computing device 200 asillustrated in FIG. 2, the CPU 340 of the mobile device 300 asillustrated in FIG. 3). The user (e.g., a doctor), to determine afeature of some disease, may need a first full quality imagereconstructed with a first image increment and/or a first imagethickness, and a second full quality image reconstructed with a secondimages increment and/or a second image thickness. Both the first fullquality image and the second full quality image may need to be processedto remove the hard tissue and/or correct the hardening beam artifact.The correction image generation unit 430 may generate different boneinformation images reconstructed with different reconstructionparameters based on the same reference image.

In 710, the image reconstruction unit 420 may generate a first image setbased on a raw data set. The first image set may include a first fullquality image and a first max FOV image. The first full quality imagemay be generated based on reconstruction parameters including FOV A1,image thickness A2, and image increment A3. The first max FOV image maybe generated based on reconstruction parameters including FOV B1, imagethickness A2, and image increment A3. In some embodiments, the FOV A1may be smaller than FOV B1. Operation of 710 may be performed accordingto the relevant portion (e.g., operation 520, operation 530, etc.) ofthe process illustrated in FIG. 5 and the description thereof.

In 720, the correction image generation unit 430 may generate areference image based on the first max FOV image. The reference imagemay be generated based on reconstruction parameters including FOV B1,image thickness M1, and image increment M2. In some embodiments, thevalue of A2 may be greater than the value of M1. In some embodiments,the value of A2 may be an integral multiple of the value of M1. In someembodiments, image thickness M1 may be the smallest image increment thatthe CT system 100 may achieve. In some embodiments, the value of A3 maybe greater than the value of M2. The value of A3 may be an integralmultiple of the value of M2. In some embodiments, the image increment M2may be the smallest image increment that the CT system 100 may achieve.The operation 720 may be performed according to the relevant portion(e.g., operation 620, operation 630, operation 640, operation 650,operation 660, etc.) of the process illustrated in FIG. 6 and thedescription thereof.

In 730, the correction image generation unit 430 may generate a firstbone information image based on the reference image. The correctionimage generation unit 430 may reconstruct a first bone information imageby stacking a certain number of reference images. The first boneinformation image may have reconstruction parameters including FOV B1,image thickness A2, and image increment A3. The operation 730 may beperformed according to the relevant portion (e.g., operation 540, etc.)of the process illustrated in FIG. 5 and the description thereof.

In some embodiments, the correction image generation unit 430 may outputthe first bone information image to a user (e.g., a doctor) via the I/O230 on the basis of which the user may distinguish the hardening beamartifact and a feature of some disease.

In 740, the correction unit 440 may correct the first full quality imagebased on the first bone information image and the first max FOV image.The correction unit 440 may correct the hardening beam artifact of thefirst full quality image based the first bone information image togenerate a beam hardening artifact corrected image. The beam hardeningartifact corrected image may include removed or reduced beam hardeningartifact. In some embodiments, the correction unit 440 may remove hardtissue of the first full quality image based on the first max FOV imageto generate a hard tissue corrected image. The hard tissue correctedimage may include removed or reduced hard tissue. The operation 740 maybe performed according to the relevant portion (e.g., operation 550,etc.) of the process illustrated in FIG. 5 and the description thereof.

In 750, the image reconstruction unit 420 may generating a second imageset based on the raw data set. The raw data set used in 730 may be sameas the raw data used in 710. The second image set may include a secondfull quality image. The second full quality image may havereconstruction parameters including FOV A1 (which is same as FOV offirst full quality image), image thickness C2, image increment C3.Operation of 750 may be performed according to the relevant portion(e.g., operation 520, operation 530, etc.) of the process illustrated inFIG. 5 and the description thereof.

In 760, the correction image generation unit 430 may generate a secondbone information image based on the reference image. The correction unit440 may reconstruct a second bone information image by stacking acertain number of reference images. The second bone information imagemay have reconstruction parameter including FOV B1, which is same as FOVof the first max FOV image and the first bone information image, imagethickness C2, and image increment C3. In some embodiments, the imagethickness C2 of the second bone information image may greater than theimage thickness M1 of the reference image. In some embodiments, theimage thickness C2 of the second bone information image may be anintegral of the image thickness M1 of the reference image. In someembodiments, the image increment C3 of the second bone information imagemay be greater than the image increment M2 of the reference image. Insome embodiments, the image increment C3 of the second bone informationimage may greater than the image increment M2 of the reference image.The image increment C3 of the second bone information image may be anintegral of the image increment M2 of the reference image. Detaileddescription of step 760 may be found in FIG. 5 and the descriptionthereof. The operation 760 may be performed according to the relevantportion (e.g., operation 540, etc.) of the process illustrated in FIG. 5and the description thereof.

In some embodiments, the correction image generation unit 430 may outputthe second bone information image to a user (e.g., a doctor) via the I/O230 to distinguish the hardening beam artifact and feature from afeature of some disease.

In 770, the correction unit 440 may correct the second full qualityimage based on the second bone information image. The correction unit440 may correct the hardening beam artifact of the second full qualityimage based on the second bone information image to generate a beamhardening artifact corrected image. The beam hardening artifactcorrected image may include removed or reduced beam hardening artifact.The operation 770 may be performed according to the relevant portion(e.g., operation 550, etc.) of the process illustrated in FIG. 5 and thedescription thereof.

FIG. 8 is a flowchart of an exemplary process for checking the workingstatus of CT system 100 based on the bone information image according tosome embodiments of the present disclosure. Different bone informationimage sets may be generated based on different raw data sets obtained byscanning the two different objects. In some embodiments, one or moreoperations of process illustrated in FIG. 8 for generating a fullquality image set may be implemented in the CT system 100 illustrated inFIG. 1. For example, the process illustrated in FIG. 8 may be stored inthe storage 150 in the form of instructions, and invoked and/or executedby the processing engine 140 (e.g., the processor 210 of the computingdevice 200 as illustrated in FIG. 2, the CPU 340 of the mobile device300 as illustrated in FIG. 3). Normally, a first bone information imageset of a first object may be similar to a second bone information imageset of a second object, since bone information image set representshardening beam artifact caused by hard tissue, and the proportion ofhard tissue and relative to soft tissue may be similar between differentobjects (e.g., different patients under examination). If the first boneinformation set is different from the second bone information set (e.g.,the similarity of the first bone information set and the second boneinformation set is under a threshold), the working status of the CTsystem 100 may have changed, and the correction unit 440 may identifyabnormalities of the CT system 100 based on whether the working statusof the CT system 100 have changed. In some embodiments, the workingstatus of the CT system 100 may include working status of the detector112 (e.g., whether the detector 112 may work at a predeterminedtemperature, etc.) and the working status of the radioactive scanningsource 115 (e.g., whether the radioactive scanning source 115 may emitpredetermined amount of X-ray).

In 810, the correction unit 440 may generate a first reference imagebased on a first raw data set. The first raw data set may include aplurality of raw data related to a first object under examination.Detailed description of 810 may be found in FIG. 6 and the descriptionthereof.

In 820, the correction unit 440 may generate a second reference imagebased on a second raw data set. The second raw data set may include aplurality of raw data related to a second object under examination.Detailed description of step 820 may be found in FIG. 6 and thedescription thereof.

In 830, the correction unit 440 may determine the working status of theCT system 100 based on the similarity between the first reference imageand the second reference image. If the similarity between the firstreference image and the second reference image exceeds a threshold, theworking status of the CT system 100 when scan the first object may bedifferent from the working status of the CT system 100 when scan thesecond object, and the. The correction unit 440 may identifyabnormalities of the CT system 100 based on difference between theworking status of the CT system 100 when scan the first object and theworking status of the CT system 100 when scan the second object. If thesimilarity between the first reference image and the second referenceimage do not exceed a threshold, working status of the CT system 100when scan the first object may be the same with the working status ofthe CT system 100 when scan the second object.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A non-transitory computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectro-magnetic, optical, or the like, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate,” or “substantially” may indicate ±20% variationof the value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A method, for CT image reconstruction, implemented on acomputing device having at least one processor, at least onecomputer-readable storage medium, the method comprising: obtaining a rawdata set related to an object; generating a set of full quality imageswith a target image thickness based on the raw data set; generating aset of original bone information images with an original image thicknessbased on the raw data set; generating a set of target bone informationimages with a target image thickness based on the set of original boneinformation images, the target image thickness being different from theoriginal image thickness; and correcting hardening beam artifact of thefull quality images based on the set of target bone information imagesto generate a set of hardening beam artifact corrected images.
 2. Themethod of claim 1, wherein the target image thickness is greater thanthe original image thickness.
 3. The method of claim 1, wherein a targetimage increment of a target bone information image of the set of targetbone information images is greater than an original image increment ofan original bone information image of the set of original boneinformation images.
 4. The method of claim 1, wherein the generating aset of original bone information images comprising: generating a set ofmax field of view images based on the raw data set; and generating theset of original bone information images based on the set of max field ofview images.
 5. The method of claim 4, wherein a field of view of a fullquality image of the set of full quality images is smaller than a fieldof view of a max field of view image of the set of max field of viewimages.
 6. The method of claim 1, further comprising: removing hardtissue from the set of full quality images based on the set of targetbone information images to generate a set of hard tissue correctedimages.
 7. The method of claim 1, wherein the generating a target boneinformation image further comprises: stacking one or more original boneinformation images based on the original image thickness and the targetimage thickness.
 8. The method of claim 1, wherein the generating atarget bone information image further comprises: stacking one or moreoriginal bone information images based on an image increment of theoriginal bone information image and an image increment of the targetbone information image.
 9. The method of claim 1, further comprising:outputting the set of target bone information images to a user.
 10. Asystem for image reconstruction, comprising: a computer-readable storagemedium storing a set of instructions for CT image reconstruction; aprocessor in communication with the computer-readable storage medium,wherein when executing the set of instructions, the system is directedto: obtain a raw data set related to an object; generate a set of fullquality images with a target image thickness based on the raw data set;generate a set of original bone information images with an originalimage thickness based on the raw data set; generate a set of target boneinformation images with a target image thickness based on the set oforiginal bone information images, the target image thickness beingdifferent from the original image thickness; and correct hardening beamartifact of the full quality images based on the set of target boneinformation images to generate a set of hardening beam artifactcorrected images.
 11. The system of claim 10, wherein the target imagethickness is greater than the original image thickness.
 12. The systemof claim 10, wherein a target image increment of a target boneinformation image of the set of target bone information images isgreater than an original image increment of an original bone informationimage of the set of original bone information images.
 13. The system ofclaim 10, wherein to generate the set of original bone informationimages comprising, the system is further directed to: generate a set ofmax field of view images based on the raw data set; and generate the setof original bone information images based on the set of max field ofview images.
 14. The system of claim 13, wherein a field of view of afull quality image of the set of full quality images is smaller than afield of view of a max field of view image of the set of max field ofview images.
 15. The system of claim 10, wherein the system is furtherdirected to: remove hard tissue from the set of full quality imagesbased on the set of target bone information images to generate a set ofhard tissue corrected images.
 16. The system of claim 10, wherein togenerate a target bone information image, the system is furtherconfigured to: stacking one or more original bone information imagesbased on the original image thickness and the target image thickness.17. The system of claim 10, wherein to generate a target boneinformation image, the system is further configured to: stack one ormore original bone information images based on an image increment of theoriginal bone information image and an image increment of the targetbone information image.
 18. The system of claim 10, wherein to generatea target bone information image, the system is further configured to:output the set of target bone information images to a user.
 19. Anon-transitory computer readable medium comprising executableinstructions that, when executed by at least one processor, cause the atleast one processor to effectuate a method comprising: obtaining a rawdata set related to an object; generating a set of full quality imageswith a target image thickness based on the raw data set; generating aset of original bone information images with an original image thicknessbased on the raw data set; generating a set of target bone informationimages with a target image thickness based on the set of original boneinformation images; and correcting hardening beam artifact of the fullquality images based on the set of target bone information images togenerate a set of hardening beam artifact corrected images.
 20. Thenon-transitory computer readable medium of claim 19, wherein the targetimage thickness is greater than the original image thickness.